MOS can be viewed as a higher-level two-class “semantic" segmentation task. Instead of distinguishing the basic semantic classes, e.g., humans, vehicles, and buildings, MOS infers the dynamic properties of objects and separates the actually moving objects, e.g., driving cars and pedestrians, from static or non-moving objects e.g., buildings and parked cars.
For point cloud segmentation, popular existing solutions can be divided into point cloud-based [1]}, [2]}, voxel-based [3]}, [4]}, and range image-based [5]}, [6]}, [7]}.
Point-based methods can extract effective features from unordered point clouds, but they are difficult to scale effectively to large-scale point cloud data.
Sparse voxels convolution [8]}, [3]} can reduce the computational burden of point clouds, but voxelization will introduce information loss.
Range images are used as a comparably lightweight intermediate representation and attractive for online applications. However, it causes boundary-blurring issues due to back-projection.
Instead of using a single representation, we propose to first use a range image-based backbone to obtain a coarse segmentation and then use a lightweight 3D voxel sparse convolution module to refine the segmentation results. Using such a coarse-to-fine architecture, our method combines the advantages of different representation modalities of LiDAR scans to alleviate the boundary-blurring issue, while maintaining good efficiency. Example results are shown in Fig. REF .
